• Contributing to the development and optimization of DBRX, Databricks open source large language

model, focusing on scalable training and inference workflows on distributed compute.

• Worked on data curation and preprocessing pipelines using Apache Spark and Delta Lake to

prepare large scale high quality training datasets for LLM development.

• Implemented model training and evaluation workflows using PyTorch and Databricks ML Runtime to

support efficient experimentation and reproducibility.

• Collaborated with research and platform teams to benchmark DBRX performance across reasoning,

summarization, and code generation tasks.

• Optimized distributed inference pipelines for DBRX on Databricks by improving batching strategies

and GPU utilization, resulting in ~22% higher throughput under production scale workloads.

• Integrated DBRX with Databricks Lakehouse architecture to enable enterprise ready deployment,

governance, and monitoring for production LLM workloads.

• Built automated evaluation and experiment tracking workflows using MLflow and Python, reducing

manual validation effort by ~30% and improving model iteration speed across releases.

• Supported fine tuning and instruction tuning efforts to adapt DBRX for enterprise use cases

including analytics assistance and internal Copilot style applications.

• Implemented resource monitoring and cost observability dashboards that helped identify inefficient

GPU usage patterns, contributing to ~15% improvement in infrastructure cost efficiency.

• Collaborated with cross functional teams to align open source model development with enterprise

security, compliance, and responsible AI guidelines.

• Worked on designing and deploying Azure OpenAI based enterprise solutions by integrating GPT

models with internal Microsoft services, enabling intelligent document processing and

conversational workflows.

• Assisted in building Copilot style AI features using Azure OpenAI, Azure Functions, and REST APIs

to automate enterprise knowledge retrieval and task recommendations for internal business teams.

• Developed data preprocessing and prompt engineering pipelines in Python to improve response

relevance and consistency for large language model powered applications.

• Collaborated with senior engineers to integrate Azure Cognitive Search with OpenAI models,

enabling semantic search and contextual question answering over enterprise documents.

• Implemented secure API based access using Azure Active Directory and role based access control

to ensure compliance with enterprise security and data governance standards.

• Supported fine tuning experiments and prompt optimization techniques that improved model

response accuracy by ~18% for domain specific enterprise use cases.

• Built logging and monitoring workflows using Azure Monitor and Application Insights to track model

latency, usage patterns, and failure scenarios in production environments.

• Worked closely with product managers and solution architects to translate business requirements

into scalable AI workflows using Azure ML and OpenAI endpoints.

• Optimized inference pipelines and token usage strategies, helping reduce operational API costs by

12% while maintaining response quality.

• Documented architecture designs, model limitations, and responsible AI considerations to support

internal reviews and enterprise client readiness.